Adaptive Neural Network Speech Recognition Runtime Weight Updates
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems face limitations in adaptability, as traditional neural networks require offline retraining, leading to inflexibility and potential performance degradation due to changing environmental conditions or shifts in spoken language patterns.
Innovation Solution
Implementing adaptive neural networks that can update their weights in real-time during runtime by utilizing speech recognition outputs, such as lattices and N-best lists, to refine their models, allowing for continuous improvement and adjustment without the need for offline retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional neural networks are used for speech recognition, then the system structure is simple and easy to implement, but the system cannot adapt to changing environmental conditions or language patterns without offline retraining
Solution Approach 1:
The patent implements dynamic adaptation by enabling the neural network to continuously update its weights during runtime based on incoming speech data. The system transitions from a static model requiring offline retraining to a dynamic model that adapts in real-time, resolving the contradiction between adaptability and complexity by making the system flexible without requiring complete retraining
Solution Approach 2:
The system incorporates feedback mechanisms where speech recognition outputs (lattices and N-best lists) are fed back into the neural network to adjust weights. This continuous feedback loop enables the system to learn from its own performance and environmental conditions, achieving adaptability while maintaining manageable complexity through incremental adjustments
2Measurement precision
If offline retraining is performed to improve recognition accuracy, then the model can be optimized for specific conditions, but the system becomes inflexible and requires downtime for updates
Solution Approach 1:
The patent enables continuous learning by updating neural network weights during runtime without requiring system downtime. The useful action of speech recognition continues uninterrupted while the model simultaneously adapts to new conditions, eliminating the need to stop operation for retraining and maintaining both accuracy and flexibility
Solution Approach 2:
The system performs self-updating by automatically adjusting its own weights based on incoming speech data and recognition outputs. This self-service capability allows the model to optimize its own performance continuously without external intervention or manual retraining, maintaining high accuracy while preserving system flexibility
3Adaptability or versatility
If the neural network weights are updated frequently to adapt to changing conditions, then the system remains effective in dynamic environments, but computational resources and processing time increase
Solution Approach 1:
The system applies partial updates by adjusting only the necessary weight parameters based on current speech inputs rather than performing complete retraining. This selective updating approach maintains adaptability to dynamic environments while significantly reducing the computational time and resources required compared to full model retraining
Solution Approach 2:
The patent implements periodic weight updates based on accumulated speech data rather than continuous updating. This periodic approach allows the system to adapt to changing conditions effectively while managing computational resources efficiently, updating weights at optimal intervals rather than continuously, thus reducing processing overhead
Data Source
AI summary
Neural networks may be used in certain automatic speech recognition systems. To improve performance of these neural networks, they may be updated/retrained during run time by training the neural network based on the output of a speech recognition system or based on the output of the neural networks themselves. The outputs may include weighted outputs, lattices, weighted N-best lists, or the like. The neural networks may be acoustic model neural networks or language model neural networks. The neural networks may be retrained after each pass through the network, after each utterance, or in varying time scales.